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A simulation-based probabilistic framework for lithium-ion battery modelling

机译：基于模拟的锂离子电池造型概率框架

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摘要

State-of-the-art researches on the modelling of lithium-ion batteries for electric vehicle have been conducted based on the physics and empirical-based models to estimate their states. However, less attention has been paid to evaluating the mechanical strength of the batteries when the battery pack is subjected to sudden external impact or crash. The present work, therefore, proposes a simulation-based probabilistic framework that combines artificial neural network and a moment-based uncertainty evaluation technique utilising the finite element model of a lithium-ion battery to evaluate its mechanical strength. The study was based on the following inputs: displacement, temperature and strain rate of the battery, and their uncertainties when the battery is subjected to sudden impact. The artificial neural network outperforms other well-known modelling methods, such as the radial basis function neural network and polynomial regression, for the global mechanical strength modelling, and the probability distribution obtained from the proposed uncertainty evaluation procedure is shown to be accurate. Further analysis employing the framework reveals that the mean mechanical strength of the battery decreases with increasing temperature, but increases with increasing displacement and strain rate. It was also found that the displacement and temperature have similarly high influence on the mechanical strength of the battery compared to the strain rate. The proposed framework and presented findings can help battery manufacturers improve the road safety of electric vehicles.
机译：基于物理学和基于经验的模型进行了对电动车辆锂离子电池的建模的最新研究，以估计其状态。然而，当电池组经受突然的外部冲击或碰撞时，已经支付了较少的注意力来评估电池的机械强度。因此，本作工作提出了一种基于模拟的概率框架，其结合了人工神经网络和利用锂离子电池的有限元模型来评估其机械强度的瞬间的基于时刻的不确定性评估技术。该研究基于以下输入：电池的位移，温度和应变率，以及当电池受到突然影响时的不确定性。人工神经网络优于其他众所周知的建模方法，例如径向基函数神经网络和多项式回归，用于全球机械强度建模，并且从所提出的不确定性评估程序获得的概率分布显示为准确。采用该框架的进一步分析表明，电池的平均机械强度随着温度的增加而降低，但随着置换和应变率的增加而增加。还发现，与应变速率相比，位移和温度对电池的机械强度相似高。拟议的框架和呈现的调查结果可以帮助电池制造商提高电动汽车的道路安全性。

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